Deep ocean drivers better explain habitat preferences of sperm whales Physeter macrocephalus than beaked whales in the Bay of Biscay

• Published in: Scientific Reports
• Main Authors: Virgili, Auriane, Teillard, Valentin, Dorémus, Ghislain, Dunn, Timothy, Laran, Sophie, Lewis, Mark, Louzao, Maite, Martínez-Cedeira, José, Pettex, Emeline, Ruiz, Leire, Saavedra, Camilo, Santos, M. Begoña, van Canneyt, Olivier, Vázquez Bonales, José Antonio, Ridoux, Vincent
• Other Authors: Observatoire pour la Conservation de la Mégafaune Marine (PELAGIS), LIttoral ENvironnement et Sociétés (LIENSs), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Joint Nature Conservation Committee (UK), Inverdee House, Basque Research and Technology Alliance (BRTA), Coordinadora para o Estudo dos Mamiferos Mariños (CEMMA), CEMMA, ADERA, Cellule Cohabys (Cohabys), AMBAR Elkartea Organisation Bizkaia, Spain, Instituto Espanol de Oceanografia, Instituto Español de Oceanografía, Alnilam Research and Conservation Madrid, Spain
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2022
• Subjects: [SDE]Environmental Sciences; North Atlantic; Physeter macrocephalus
• Online Access: https://hal.science/hal-03695769; https://hal.science/hal-03695769/document; https://hal.science/hal-03695769/file/s41598-022-13546-x.pdf; https://doi.org/10.1038/s41598-022-13546-x

International audience Abstract Species Distribution Models are commonly used with surface dynamic environmental variables as proxies for prey distribution to characterise marine top predator habitats. For oceanic species that spend lot of time at depth, surface variables might not be relevant to predict deep-dwelling prey distributions. We hypothesised that descriptors of deep-water layers would better predict the deep-diving cetacean distributions than surface variables. We combined static variables and dynamic variables integrated over different depth classes of the water column into Generalised Additive Models to predict the distribution of sperm whales Physeter macrocephalus and beaked whales Ziphiidae in the Bay of Biscay, eastern North Atlantic. We identified which variables best predicted their distribution. Although the highest densities of both taxa were predicted near the continental slope and canyons, the most important variables for beaked whales appeared to be static variables and surface to subsurface dynamic variables, while for sperm whales only surface and deep-water variables were selected. This could suggest differences in foraging strategies and in the prey targeted between the two taxa. Increasing the use of variables describing the deep-water layers would provide a better understanding of the oceanic species distribution and better assist in the planning of human activities in these habitats.